LIME is another popular additive interpretability method [1]}. However, it suffers from previously described restricting hypotheses as it relies on a surrogate linear model to locally approximate influence of each variable, through multiple random perturbations of the instance to explain. This random nature of LIME leads to an high instability, especially compared to SHAP [2]}. Thus we will focus in this article on Shapley-based interpretability methods.
